Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Mixing independent classifiers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
UCSpv: principled voting in UCS rule populations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Adaptive mixtures of local experts
Neural Computation
Bayesian hierarchical mixtures of experts
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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In this paper we promote a new methodology for designing LCS that is based on first identifying their underlying model and then using standard machine learning methods to train this model. This leads to a clear identification of the LCS model and makes explicit the assumptions made about the data, as well as promises advances in the theoretical understanding of LCS through transferring the understanding of the applied machine learning methods to LCS. Additionally, it allows us, for the first time, to give a formal and general, that is, representation-independent, definition of the optimal set of classifiers that LCS aim at finding. To demonstrate the feasibility of the proposed methodology we design a Bayesian LCS model by borrowing concepts from the related Mixtures-of-Experts model. The quality of a set of classifiers and consequently also the optimal set of classifiers is defined by the application of Bayesian model selection, which turns finding this set into a principled optimisation task. Using a simple Pittsburgh-style LCS, a set of preliminary experiments demonstrate the feasibility of this approach.